Big Data Analytics and Artificial Intelligence for Sustainable Omnichannel Marketing in the FMCG Industry: A Conceptual Framework

Utkarsh Singh Parihar; Piyush Prakash

Authors

Utkarsh Singh Parihar Research Scholar, DAV College,CSJMU, Kanpur Author
Piyush Prakash Associate Professor, DAV College, CSJMU, Kanpur Author

Keywords:

Artificial Intelligence, Big Data Analytics, Sustainable Marketing, Omnichannel Marketing, FMCG Industry, Consumer Analytics, Consumer Trust, Customer Experience, Purchase Intention, Customer Loyalty

Abstract

The rapid digital transformation of the Fast-Moving Consumer Goods (FMCG) industry has significantly reshaped marketing practices by enabling organizations to engage consumers across multiple digital and physical touchpoints. The increasing adoption of Artificial Intelligence (AI) and Big Data Analytics (BDA) has enhanced firms' ability to deliver personalized customer experiences while simultaneously supporting sustainable business practices. Although prior studies have examined AI, Big Data Analytics, sustainability, and omnichannel marketing independently, limited research has integrated these interrelated concepts into a comprehensive conceptual framework. To address this gap, the present study adopts an integrative literature review approach to synthesize contemporary research and develop a conceptual framework explaining the relationships among AI capability, Big Data Analytics capability, sustainable omnichannel marketing, sustainable customer experience, consumer trust, and purchase intention within the FMCG industry. The review primarily considers peer-reviewed studies published between 2020 and 2026, as this period reflects the rapid advancement of AI technologies, accelerated digital transformation following the COVID-19 pandemic, and the growing emphasis on sustainability in marketing research. Earlier seminal studies were also consulted where necessary to establish theoretical foundations. Based on the synthesized literature, the study proposes that AI and Big Data Analytics jointly enhance sustainable omnichannel marketing by improving personalization, customer experience, and organizational transparency, which subsequently foster consumer trust and purchase intention. The proposed framework also conceptualizes environmental consciousness as a moderating factor influencing sustainable consumer behaviour. The originality of this study lies in integrating Resource-Based View (RBV), Dynamic Capabilities Theory (DCT), and the Technology–Organization–Environment (TOE) framework to provide a unified theoretical explanation of AI-enabled sustainable omnichannel marketing. The proposed conceptual model offers a foundation for future empirical research while providing valuable strategic insights for FMCG organizations seeking to achieve sustainable competitive advantage through intelligent marketing capabilities.

References

Akter, S., Wamba, S. F., Gunasekaran, A., Dubey, R., & Childe, S. J. (2016). How to improve firm performance using big data analytics capability and business strategy alignment. International Journal of Production Economics, 182, 113–131. https://doi.org/10.1016/j.ijpe.2016.08.018

Bag, S., Pretorius, J. H. C., Gupta, S., & Dwivedi, Y. K. (2021). Role of institutional pressures and resources in the adoption of big data analytics powered artificial intelligence, sustainable manufacturing practices and circular economy capabilities. Technological Forecasting and Social Change, 166, 120420. https://doi.org/10.1016/j.techfore.2020.120420

Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99–120. https://doi.org/10.1177/014920639101700108

Belhadi, A., Kamble, S., Jabbour, C. J. C., Gunasekaran, A., Ndubisi, N. O., et al. (2021). Manufacturing and service supply chain resilience to the COVID-19 outbreak: Lessons learned from the automobile and airline industries. Technological Forecasting and Social Change, 163, 120447. https://doi.org/10.1016/j.techfore.2020.120447

Birkel, H., & Müller, J.M. (2020). Potentials of industry 4.0 for supply chain management within the triple bottom line of sustainability – A systematic literature review. Journal of Cleaner Production, 289. https://doi.org/10.1016/j.jclepro.2020.125612

Brau, R. I., Sanders, N. R., Aloysius, J., & Williams, D. (2023). Utilizing people, analytics, and AI for decision making in the digitalized retail supply chain. Journal of Business Logistics, 45, e12355. https://doi.org/10.1111/jbl.12355

Brindha, G., Vidya, M., & Reshma, M. (2025). AI-Driven Omnichannel Marketing and Customer Experience: A Bibliometric Overview. Indian Journal of Marketing, 55(11), 69–92. https://doi.org/10.17010/ijom/2025/v55/i11/175822

Brynjolfsson, E., Hu, Y. J., & Rahman, M. S. (2013). Competing in the age of omnichannel retailing. MIT Sloan Management Review, 54(4), 23–29.

Cai, Y. J., & Lo, C. K. Y. (2020). Omni-channel management in the new retailing era: A systematic review and future research agenda. International Journal of Production Economics, 229, 107729. https://doi.org/10.1016/j.ijpe.2020.107729

Chalmeta, R., & Santos-de-León, N. J. (2020). Sustainable Supply Chain in the Era of Industry 4.0 and Big Data: A Systematic Analysis of Literature and Research. Sustainability, 12(10), 4108. https://doi.org/10.3390/su12104108

Chintalapati, S., & Pandey, S. K. (2021). Artificial intelligence in marketing: A systematic literature review. Journal of Creative Communications, 17(1), 38–68. https://doi.org/10.1177/14707853211018428

Cui, T. H., Ghose, A., Halaburda, H., Iyengar, R., Pauwels, K., et al. (2021). Informational Challenges in Omnichannel Marketing: Remedies and Future Research. Journal of Marketing, 85(1), 103–120. https://doi.org/10.1177/0022242920968810

Darbanian, F., Brandtner, P., Falatouri, T., & Nasseri, M. (2024). Data Analytics in Supply Chain Management: A State-of-the-Art Literature Review. Operations and Supply Chain Management: An International Journal, 17(1), 1-31. https://doi.org/10.31387/oscm0560411

D'Arco, M., Lo Presti, L., Marino, V., & Resciniti, R. (2019). Embracing AI and big data in customer journey mapping: From literature review to a theoretical framework. Innovative Marketing, 15(4), 102–115. http://dx.doi.org/10.21511/im.15(4).2019.09

Davenport, T., Guha, A., Grewal, D., & Bressgott, T. (2020). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48(1), 24–42. https://doi.org/10.1007/s11747-019-00696-0

De Bock, K. W., Das, R., & Filieri, R. (2026). A taxonomy of machine learning, AI, and generative AI in marketing: Recent applications and future directions. Journal of Business Research, 216, 116346. https://doi.org/10.1016/j.jbusres.2026.116346

De Mauro, A., Sestino, A., & Bacconi, A. (2022). Machine learning and artificial intelligence use in marketing: A general taxonomy. Italian Journal of Marketing, 439–457. https://doi.org/10.1007/s43039-022-00057-w

Dubey, R., Gunasekaran, A., Childe, S. J., Fosso Wamba, S., Roubaud, D., et al. (2021). Empirical investigation of data analytics capability and organizational flexibility as complements to supply chain resilience. International Journal of Production Research, 59(1), 110–128. https://doi.org/10.1080/00207543.2019.1582820

Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., et al. (2021). Artificial intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002

Grewal, D., Noble, S. M., Roggeveen, A. L., & Nordfält, J. (2020). The future of in-store technology. Journal of the Academy of Marketing Science, 48(1), 96–113. https://doi.org/10.1007/s11747-019-00697-z

Huang, M. H., & Rust, R. T. (2021). A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science, 49(1), 30–50. https://doi.org/10.1007/s11747-020-00749-9

Ivanov, D., & Dolgui, A. (2020). A digital supply chain twin for managing the disruption risks and resilience in the era of Industry 4.0. Production Planning & Control, 32(9), 775–788. https://doi.org/10.1080/09537287.2020.1768450

Kumar, A., Abhishek, K., Ghalib, M.R., Nerurkar, P., Bhirud, S., et al. (2021). Securing logistics system and supply chain using Blockchain. Applied Stochastic Models in Business and Industry, 37, 413-428. https://doi.org/10.1002/asmb.2592

Kumar, R.R., & Raj, A. (2025). Big data adoption and performance: mediating mechanisms of innovation, supply chain integration and resilience. Supply Chain Management: An International Journal, 30(1), 67-85. https://doi.org/10.1108/SCM-03-2024-0186

Lemon, K. N., & Verhoef, P. C. (2016). Understanding customer experience throughout the customer journey. Journal of Marketing, 80(6), 69–96. https://doi.org/10.1509/jm.15.0420

Pantano, E., Pizzi, G., Scarpi, D., & Dennis, C. (2020). Competing during a pandemic? Retailers' ups and downs during the COVID-19 outbreak. Journal of Business Research, 116, 209–213. https://doi.org/10.1016/j.jbusres.2020.05.036

Payne, E. M., Peltier, J. W., & Barger, V. A. (2017). Omni-channel marketing, integrated marketing communications and consumer engagement: A research agenda. Journal of Research in Interactive Marketing, 11(2), 185–197. https://doi.org/10.1108/JRIM-08-2016-0091

Queiroz, M. M., Ivanov, D., Dolgui, A., & Fosso Wamba, S. (2022). Impacts of epidemic outbreaks on supply chains: Mapping a research agenda amid the COVID-19 pandemic through a structured literature review. Annals of Operations Research, 319(1), 1159–1196. https://doi.org/10.1007/s10479-020-03685-7

Seyedan, M., & Mafakheri, F. (2020). Predictive big data analytics for supply chain demand forecasting: Methods, applications, and research opportunities. Journal of Big Data, 7(53). https://doi.org/10.1186/s40537-020-00329-2

Shankar, V., Kalyanam, K., Setia, P., Golmohammadi, A., Tirunillai, S., et al. (2021). How technology is changing retail. Journal of Retailing, 97(1), 13–27. https://doi.org/10.1016/j.jretai.2020.10.006

Sharma, A. P., Starčević, S., & Saha, R. (2024). Determinants of customer buying behaviour in omnichannel retailing: A systematic literature review and future research directions. Journal of Service Theory and Practice, 34(6), 822–863. https://doi.org/10.1108/JSTP-07-2023-0213

Singh, D., Sharma, A., Singh, R. K., & Rana, P. S. (2025). Artificial intelligence enabled supply chain resilience: Insights from FMCG industry. Journal of Global Operations and Strategic Sourcing, 18(2), 414–441. https://doi.org/10.1108/JGOSS-02-2024-0017

Smyth, C., Dennehy, D., Wamba, S. F., Scott, M., & Harfouche, A. (2024). Artificial intelligence and prescriptive analytics for supply chain resilience: A systematic literature review and research agenda. International Journal of Production Research, 62(23), 8537–8561. https://doi.org/10.1080/00207543.2024.2341415

Teece, D. J. (2018). Business models and dynamic capabilities. Long Range Planning, 51(1), 40–49. https://doi.org/10.1016/j.lrp.2017.06.007

Verhoef, P. C., Kannan, P. K., & Inman, J. J. (2015). From Multi-Channel Retailing to Omni-Channel Retailing: Introduction to the Special Issue on Multi-Channel Retailing. Journal of Retailing, 91(2), 174–181. https://doi.org/10.1016/j.jretai.2015.02.005

Wamba, S. F., Gunasekaran, A., Akter, S., Ren, S. J. F., Dubey, R., et al. (2017). Big data analytics and firm performance: Effects of dynamic capabilities. Journal of Business Research, 70, 356–365. https://doi.org/10.1016/j.jbusres.2016.08.009

Wang, S., & Yan, R. (2022). A global method from predictive to prescriptive analytics considering prediction error for “Predict, then optimize” with an example of low-carbon logistics. Cleaner Logistics and Supply Chain, 4, 100062. https://doi.org/10.1016/j.clscn.2022.100062

Wedel, M., & Kannan, P. K. (2016). Marketing analytics for data-rich environments. Journal of Marketing, 80(6), 97–121. https://doi.org/10.1509/jm.15.0413